MURI: QUANTA: Quantitative Network-based Models of Adaptive Team Behavior

Multidisciplinary University Research Initiative (MURI) on the Network Science of Teams

The recent convergence of research in social and psychological sciences, dynamic and quantitative modeling, and network science has led to a re-examination of collective team behavior from a quantitative and systems-oriented viewpoint. Teams cannot be understood fully by studying their components (members) in isolation: team performance is not simply a sum of individual performances; and a diversity of opinions among members leads to better group outcomes. However, it is not yet understood how patterns of interactions and relationships among team members (i.e. team networks) impact performance. Understanding these patterns is critical, as the resolution of complex issues requires deliberative within-group interaction processes in which alternative courses of action are surfaced, evaluated, and acted upon. This project aims to build quantifiable informative models of teams as dynamical systems interacting over multiple networks, analyze dynamic team behavior by developing rigorous models that relate interaction patterns and network evolution to task performance, and break new ground in team design by scaling teams to solve complex tasks (i.e. teams of teams), and advancing social science theories of team performance. Besides UCSB, our team includes leading scientists from MIT, USC, UIUC, and Northwestern.

Funded by: Army Research Office, Award# W911NF-15-1-0577

Affiliated People

Research interests: 

Applied Machine Learning, Complex Network Analysis, Convex Optimization, Multi-Agent Systems, Natural Language Processing

Omid received a B.Sc in Computer Engineering in 2011 and M.Sc. in Artificial Intelligence in 2014 from Sharif University of Technology, Tehran, Iran. Prior to joining Dynamo lab in 2015, he spent few years as a software engineer in industry. He has a background in complex networks, analysis of financial data and applied machine learning.

Photo of Zexi Huang.
Research interests: 

Graph Data Mining, Representation Learning

Zexi received his B.Eng. in Computer Science and Technology at University of Electronic Science and Technology of China, Chengdu in 2018. He joined Dynamo lab in 2018. His research interests span the analysis of social, informational, and biological networks with machine learning and data mining techniques. 

Research interests: 

Data Mining, Applied Machine Learning, Network Science.

Mert received a B.Sc in Computer Science and Engineering in 2018 from Sabanci University, Istanbul. He joined Dynamo lab as a Ph.D. student in 2018. His research interests include machine learning on graphs, explainability, and human-ai interactions. He is exploring novel graph neural network algorithms to solve graph tasks such as graph classification and link prediction on the different types of networks like social, communication, and molecular. He previously worked on differential privacy on recommendation systems using graph data in his bachelor's.

Research interests: 

Machine learning, data mining and network science. Specifically, clustering, semi-supervised learning, classification, relational learning, and causal reasoning.

Wei is a postdoctoral researcher with the DYNAMO lab. He received his Dr.rer.nat (PhD) degree in Computer Science from Lugwig-Maximilians University of Munich in 2018. Before joining the DYNAMO lab, he worked as a researcher in the Department of AI Platform, Tencent Inc. China.